Data detection in a sequence of tokens using decision tree reductions

ABSTRACT

An apparatus for processing a sequence of tokens to detect predetermined data, wherein each said token has a token type, and the predetermined data has a structure that comprises a predetermined sequence of token types, including at least one optional token type. The apparatus comprises a processor arranged to: provide a tree for detecting the predetermined data, the tree comprising a plurality of states, each said state being linked with at least one other state by a respective condition, the arrangement of linked states forming a plurality of paths; and compare the token types of the sequence of tokens to respective conditions in the tree to match the sequence of tokens to one or more paths in the tree, wherein the predetermined data can be detected without using an epsilon reduction to take account of said at least one optional token type.

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Patent Application No. 61/059,722, filed on Jun. 6, 2008, which is herein incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to the field of processing sequences of characters and, in addition, to detecting data included in such sequences of characters.

BACKGROUND OF THE INVENTION

The processing of sequences of characters to analyze their grammatical structure is well-known, both for analyzing natural languages and computer languages. In the case of natural languages, the sequences of characters are broken down into words, each word forming a part of speech, such as noun, verb, adjective, adverb, preposition and so on. Thus, each word can be allocated a class according to its function in context.

For the processing of computer languages, it is well known to process the sequence of characters in a lexer to break the characters into a sequence of tokens and then to parse the tokens to create some form of internal representation, which can then be used in a compiler or an interpreter.

Such processing has previously been used to analyze sequences of characters to extract useful information from the sequence. For example, techniques have been developed to analyze blocks of text, such as e-mails or other data received by or input to a computer, to extract information such as e-mail addresses, telephone and fax numbers, physical addresses, IP addresses, days, dates, times, names, places and so forth. In one implementation, a so-called data detector routinely analyses incoming e-mails to detect such information. The detected information can then be extracted to update the user's address book or other records.

Conventionally, such data detection is performed using a layered engine as shown in FIG. 1. The engine is embodied in a processor 1 and comprises a lexical analyzer or lexer 10 and a parser 20. The lexer 10 receives as its input a sequence of characters, such as the characters in an e-mail message. Note that the characters are not limited to letters or even numbers, but may include any other characters, such as punctuation.

The lexer 10 stores a vocabulary that allows it to resolve the sequence of characters into a sequence of tokens. Each token comprises a lexeme (analogous to a word) and a token type (which describes its class or function). One token type is provided for each predetermined function. As an example, a simple lexer 10 may include the following vocabulary:

DIGIT := [0-9] (A digit is a single number from 0 to 9) NUMBER := DIGIT+ (A number is two or more digits together) LETTER := [a-zA-Z] (A letter is an upper or lower case letter from A-Z) WORD := LETTER+ (A word is two or more letters together)

The lexer 10 would break down the string of characters “There are 2 books and 15 magazines” into the following tokens:

Lexeme Token Type THERE WORD ARE WORD  2 DIGIT BOOKS WORD AND WORD 15 NUMBER MAGAZINES WORD

The parser 20 receives the sequence of tokens from the lexer 10. The parser 20 includes a grammar, which it uses to analyze the tokens to extract predetermined data. For example, if the engine 1 is intended to detect all quantities, the parser 20's grammar may be that:

QUANTITY:=DIGIT WORD|NUMBER WORD

where “|” indicates “or”. Thus, on receiving the sequence of tokens from the lexer 10, the parser 20 will return the quantities “2 books” and “15 magazines”.

Commonly, both the lexer 10 and the parser 20 use a decision tree. An example of such a decision tree for a further example of a lexer 10 is shown in FIG. 2. In this case, the lexer 10 includes the following vocabulary:

-   -   a:=1 9 [0-9]{2}     -   b:=1 9 5         where ‘a’ and ‘b’ are two token types that the lexer 10 can         ascribe to different lexemes. The decision tree in FIG. 2 shows         5 possible states in addition to the start state. As the lexer         10 processes a sequence of characters, it checks the first         character in the sequence against the options available at the         start state S and proceeds according to the result.

For example, if the lexer 10 is presented with the sequence of characters ‘1984’, it will process the character ‘1’ first. State S only allows the processing to proceed if the first character is ‘1’. This condition is met so character ‘1’ is consumed and processing proceeds to state 1, where the next character in the sequence (‘9’) is compared with the available conditions. It should be noted that state 1 is represented using a dotted circle. This is indicative that processing may not end at this state without the branch dying, as will become apparent later.

The only available condition at state 1 is that the next character is ‘9’. This condition is met, so character ‘9’ is consumed and processing proceeds to state 2.

The conditions at state 2 are that processing should proceed to state 3 if the next character is ‘5’, or that it should proceed to state 4 if the next character is any one of 0, 1, 2, 3, 4, 6, 7, 8 or 9. Again, state 2 is represented using a dotted circle and processing may not end at this state.

The next character is ‘8’, which meets the condition for processing to proceed to state 4, which is also represented by a dotted circle. Accordingly, the ‘8’ is consumed and processing continues. Since the next character in the sequence (‘4’) meets the only available condition from state 4, processing proceeds to state 5.

State 5 is represented by a solid circle, indicating that processing may end there. As shown in FIG. 2, state 5 has the property of reducing the consumed characters to a token of token type ‘a’. In our example, since all the characters have been used up and there are no more characters, processing ends at state 5 and the consumed sequence of characters is reduced to a token comprising the lexeme ‘1984’ and the token type ‘a’.

Similarly, the lexer 10 in FIG. 2 would process the sequence of characters ‘195’ as set out below. First, characters ‘1’ and ‘9’ would be consumed in the same manner as described above. However, at state 2, the next character is ‘5’. This meets the condition for proceeding to state 3, which has the property of reducing the consumed characters to a token of token type ‘b’. In this case, since all the characters have been used up and there are no more characters, processing ends at state 3 and the consumed sequence of characters is reduced to a token comprising the lexeme ‘1985’ and the token type ‘b’.

By contrast, the lexer 10 in FIG. 2 would process the sequence of characters ‘1955’ as set out below. First, characters ‘1’, ‘9’ and ‘5’ would be consumed in the same manner as described above. However, at state 3, not all the characters have been used up. Rather, a further ‘5’ remains, which meets the condition for proceeding to state 5, where the consumed sequence of characters is reduced to a token comprising the lexeme ‘1955’ and the token type ‘a’.

Now consider a parser 20 including the following grammar:

A:=a|ε

E:=Acd|ce

where A and E are predetermined grammatical or data categories that we wish to detect; a, c, d and e are various token types; and ε represents a “nothing”. Thus, the parser 20 outputs a category A if either a lexeme with token type ‘a’ is presented or an unmatched token type is presented. Similarly, the parser 20 outputs an E when it processes Acd or ce. However, since the parser 20 outputs an A when presented with a token type ‘a’ or with a nothing, by substituting the equation for A into the equation for E, it can be seen that in practice the parser 20 outputs an E when it processes any of acd, cd and ce.

A decision tree for this grammar is shown in FIG. 3 and includes start state S, finish state F, and processing states 0-5. As the parser 20 processes a sequence of tokens, it checks the first token in the sequence against the options available at the start state S and proceeds according to the result.

For example, if the parser 20 is presented with the sequence of tokens comprising a token having token type c, followed by a token having token type e, the parser 20 must process the token-type sequence ‘ce’. The following table represents the processing that takes place.

Current state Sequence to process Previous states S ce 0 e S 1 S, 0 S E F S

Put simply, proceeding from the start state S, the parser 20 consumes a ‘c’ and proceeds to state 0, and then consumes an ‘e’ and proceeds to state 1. State 1 allows processing to finish with the reduction to go back two states and replace the consumed letters by an ‘E’. Processing then returns to the start state S, where the E is processed. The E is consumed as processing proceeds to the finish state F. Thus, the token type sequence c followed by e is parsed as having the grammatical or data type E.

Similarly, the token sequence ‘acd’ is processed using the parsing tree shown in FIG. 3 as shown in the following table:

Current state Sequence to process Previous states S acd 5 cd S S Acd 2 cd S 3 d S, 2 4 S, 2, 3 S E F S

Here, the first token type to be parsed is ‘a’. Starting at start state S, the ‘a’ is consumed and processing proceeds to state 5, which has the reduction to go back one state and replace the consumed items with an ‘A’. Thus, the sequence is changed from ‘acd’ to ‘Acd’ and processing returns to state S, where the A is consumed and processing proceeds to state 2. Next, as processing proceeds along the middle branch of the tree to states 3 and 4, the c and the d are consumed. At state 4, the consumed sequence Acd is replaced by an E and processing returns to state S, where the E is processed. The E is consumed as processing proceeds to the finish state F. Thus, the token type sequence a followed by c followed by d is also parsed as having the grammatical or data type E.

Similarly, the token sequence ‘cd’ is processed using the parsing tree shown in FIG. 3 as shown in the following table:

Current state Sequence to process Previous states S cd S Acd 2 cd S 3 d S, 2 4 S, 2, 3 S E F S

Here, the first token type to be parsed is ‘c’. Starting at start state S, the ‘c’ is consumed and processing proceeds to state 0. The next token type to be parsed is a ‘d’, but state 0 does not provide an option for proceeding with this token type. Moreover, state 0 is represented by a dotted circle, indicating that processing cannot finish at that state. Accordingly, this branch is a “dead” branch and processing reverts with the entire sequence intact to the start state S. This state is provided with the reduction that an ‘A’ must be placed at the front of the sequence. Thus, the sequence to be parsed is now ‘Acd’. This is the same sequence as is generated during processing of the sequence acd above, and processing proceeds in exactly the same way. Thus, the token sequence c followed by d is also parsed as having the grammatical or data type E.

In particular, the example illustrates how the epsilon symbol is handled. Specifically, an additional path is provided, the additional path comprising a link between the start state S and state 5. This path is taken when the first token is an ‘a’, which is consequently consumed and replaced with an ‘A’.

Importantly, the when all of the conditions of the start S lead to a dead branch, the reduction associated with the start state S is performed. This reduction involves producing a new token (in this case an ‘A’) and adding it to the front of the sequence of tokens without first consuming a token. Put another way, in this reduction the sequence of tokens is revised by adding a token to the beginning and then comparing the revised sequence with the conditions of the same state. This type of reduction is known as an epsilon reduction.

In this way, it can be seen that the parsing tree shown in FIG. 3 is consistent with the grammar:

A:=a|ε

E:=Acd″ce

-   -   The foregoing is a simple explanation of the basic functionality         of lexers 10 and parsers 20. This functionality can be adapted         to detect predetermined types of data from a sequence of         characters, for example in an e-mail or a block of text. Imagine         that it is intended to detect either a time or a bug         identification code in a block of text. In the following         example, the format of a time to be detected is that it is         always one of AM, PM, A or P followed by two digits, whereas the         format of a bug identification code to be detected is always two         letters followed by three digits. Accordingly, the lexer 10 may         be provided with the vocabulary:

INITIALS := [A-Z]{2} (INITIALS is any two letters together) MERIDIAN := (A|P) M? (MERIDIAN is the letter A or the letter P, optionally followed by the letter M) DIGIT := [0-9] (DIGIT is any character from 0 to 9) whereas the parser 20 may be provided with the grammar:

BUG_ID := INITIALS DIGIT{3} (INITIALS token followed by 3 DIGIT tokens) TIME := MERIDIAN DIGIT{2} (MERIDIAN token followed by 2 DIGIT tokens)

In more detail, the lexer 10 will output a sequence of a letter from A to Z followed by another letter from A to Z as a token having a lexeme of the two letters and having the token type INTIALS. It will also output the letters AM and PM as a token having the token type MERIDIAN. In this notation ‘?’ indicates that the preceding character(s) may or may not be present. Thus, the lexer 10 will also output the letter A alone, or the letter P alone as a token having the token type MERIDIAN.

FIG. 4 shows a decision tree of the lexer 10 and FIG. 5 shows a decision tree of the parser 20. As will be clear from following the decision tree shown in FIG. 4, the lexer 10 will process the sequence of characters AM02 and output four tokens. The first is a token having the lexeme AM and the token type INITIALS, while the second is a token also having the lexeme AM, but this time the token typeMERIDIAN. This is consistent with the vocabulary used by the lexer 10, since the letters AM can be either INITIALS or a MERIDIAN. The third and fourth tokens have the lexemes ‘0’ and ‘2’ respectively and each has the token type DIGIT. This sequence of three tokens is then operated on by the parser 20.

As noted above, the two tokens both have the lexeme AM and the respective token types INITIALS and MERIDIAN. Accordingly, when the character string AM occurs, two sequences of tokens are processed by the parser 20 using the decision tree shown in FIG. 5. One sequence of tokens meets the first condition of the starting state, while the other sequence of tokens meets the other condition. Accordingly both conditions or branches are investigated, either in turn or in parallel.

In the case of the left-hand INITIALS branch, the processing proceeds to state 1 and then states 2 and 3, since the next two tokens have the token type DIGIT. However, the parser 20 then runs out of tokens to parse and so cannot proceed to state 4. Since state 3 is represented by a dotted circle, processing cannot end there and so a BUG_ID is not detected.

In the case of the right-hand MERIDIAN branch, the processing proceeds to state 5 and then states 6 and 7, since the next two tokens have the token type DIGIT. At state 7 it is determined that the sequence of tokens MERIDIAN followed by DIGIT and DIGIT represents TIME. In this way, a time is detected.

In some cases, in real life situations it is possible to detect two different types of information (eg TIME and BUG_IDENTIFICATION) from the same sequence of characters, for example where the results are overlapping. For instance in the BUG_ID/TIME example, consider the character sequences “AM12” in “AM123”. Within “AM123” we could recognize both a time (characters 1 to 4), and a bug identification code (characters 1 to 5). In such an event, it is common practice to provide an additional filter to determine which of the two detected types of information is more likely to be the correct one. One commonly-used heuristic that has proven efficient is to keep only the longest result—in this case, the bug identification code.

As another example, the parser may be provided with the grammar

ADDRESS:=name? company? street

Accordingly, to detect an address, it is only necessary for a street to be present, a name and/or a company in front of the street being optional. Thus, an epsilon reduction is required for both the name and company. Using the tokens a, b and, c, the grammar can be rewritten as

a:=name|ε

b:=company|ε

c:=street

ADDRESS:=a b c

FIG. 6 shows a corresponding decision tree for the parser 20, which determines that an address has been detected when it reaches state F. In this case, the fact that the “name” token is optional is handled by the path from the starting state S to state 1, the reduction for state 1 and the epsilon reduction for starting state S. Similarly, the fact that the “company” token is optional is handled by the path from state 2 to state 5, the reduction for state 5 and the epsilon reduction for state 2.

Such a methodology can be applied to many different types of grammar and data structures and has previously been found to be particularly successful in extracting predetermined types of data from sequences of characters. However, in view of the increasing calls on the processors of user and server computers to carry out numerous tasks (including data detection), combined with the increasing volume of information that needs to be scanned and the increasingly complex and numerous types of information it is desired to detect, it is desirable to increase the speed with which such data detection can be carried out.

SUMMARY OF THE DESCRIPTION

Method, systems and machine-readable storage media for processing a sequence of tokens are described. In one embodiment, a method for processing a sequence of tokens includes providing a tree for detecting predetermined data that has a structure that includes a predetermined sequence of token types and comparing the token types of the sequence of tokens to respective conditions in the tree to match the sequence of tokens to one or more paths in the tree. The predetermined data can, in one embodiment, be detected without an epsilon reduction to take into account of at least one optional token type. The tree includes a plurality of states, each being linked with at least one other state by a respective condition, and the arrangement of linked states form a plurality of paths. Other methods are also described, and systems and machine-readable storage media which perform these methods are also described.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will now be described by way of further example only and with reference to the accompanying drawings, in which:

FIG. 1 is a schematic representation of a parsing apparatus;

FIG. 2 shows a decision tree of a lexer according to the known art;

FIG. 3 shows a decision tree of a parser according to the known art;

FIG. 4 shows a decision tree of another lexer according to the known art;

FIG. 5 shows a decision tree of another parser according to the known art;

FIG. 6 shows another decision tree of another parser according to the known art;

FIG. 7 shows a decision tree of a parser according to the present invention;

FIG. 8 shows a further decision tree according to the present invention;

FIG. 9 shows a yet further decision tree according to the present invention;

FIG. 10 shows a still further decision tree according to the present invention;

FIG. 11 shows a system in the present invention; and

FIG. 12 shows one embodiment of a computer system.

DETAILED DESCRIPTION

The present invention is generally related to lexing and parsing sequences of characters. In particular, some embodiments of the present invention related to the removal of epsilon reductions in lexing and parsing characters in order to improve the overall efficiency.

It is noted that where an optional token type is included, processing for that optional token will always use the same branch or path. Accordingly, in the example in FIG. 3, a path including the link S-2 (path S-2-3-4) is followed both when an ‘a’-type token is included in the sequence of tokens being processed (acd) and when an ‘a’-type token is not included (cd). Similarly, in FIG. 6 a path including the link S-2 (path S-2-3-4) is always followed both when the optional ‘name’ token type is included in the sequence (name company street; name street) and when it is not (company street; street). Again, in FIG. 6 a path including the link 2-3 (path S-2-3-4) is always followed both when the optional ‘company’ token type is included in the sequence (name company street; company street) and when it is not (name street; street). This appears an elegant and efficient way of processing data.

However, processing is particularly wasteful when the data to be detected does not exist within the sequence. For example, consider using the decision tree shown in FIG. 3 to attempt to detect an E in the sequence ‘gcd’. Since g is the first token type, none of the conditions of the starting state S is met. Accordingly, the epsilon reduction for the starting state S is used and an ‘A’ is added to the front of the sequence, so that the current sequence becomes ‘Agcd’. Subsequently, the ‘A’ is consumed and processing proceeds from state S to state 2. The condition for state 2 is not met and state 2 is represented by a dotted circle, which means that processing cannot end with at state 2. The branch therefore dies uselessly. It will be apparent that when scanning a large block of text, such wasted processing will occur a large number of times just to handle a single epsilon reduction in a query for a single, simple type of predetermined data. In real life examples, a huge amount of processing capacity is wasted in investigating such dying branches.

Moreover, even when the type of data to be detected exists within the sequence of tokens being processed, processing is inefficient. In particular, it is necessary to convert the token from ‘a’ to an ‘A’, requiring the path from state S to state 5 and the reduction for state 5, and it is also necessary to provide the epsilon reduction at state S to pop up an ‘A’ in front of the current sequence according to the conventional approach. In either case, two lots of processing must take place—the initial processing to cope with the fact that the token type is optional, by revising the sequence to include an ‘A’ in it, and the subsequent processing to detect the data using the revised sequence. Thus, the conditions of state S must be checked against twice in the processing.

Where two or more epsilon reductions are included in a conventional decision tree, as shown in FIG. 6, the reduction in efficiency is significantly compounded.

Some embodiments of the present invention provide a solution to improve the efficiency of lexing and parsing, which are described below by way of example.

In some embodiments, the solution provided by the present invention is to remove epsilon reductions from the decision tree. More particularly, states are removed from the decision tree that produce an additional token without reverting to an earlier state (that is, without a token being consumed) when all of the conditions of that state are unmatched or lead to dying branches. This is achieved by instead providing an additional path in the tree, so that one path can directly be used to detect the required data when the token sequence includes the optional token type and another, separate path can directly be used to detect the required data when the token sequence does not include the optional token type.

As an example, the decision tree of FIG. 3 modified in accordance with some embodiments of the present invention is shown in FIG. 7. Here it can be seen that the epsilon reduction for the starting state has been removed and instead a new path has been provided in the tree. The new path is formed by new state 6 and the link between it and state 0. To reach state 6, a new condition has been added to state 0. Moreover, the starting state is now represented by a dotted circle, indicating that processing cannot end there.

For example, if the decision tree used in FIG. 7 is used to process the token-type sequence ‘ce’, the following table represents the processing that takes place.

Current state Sequence to process Previous states S ce 0 e S 1 S, 0 S E F S

Similarly, the token sequence ‘acd’ is processed using the parsing tree shown in FIG. 7 as shown in the following table:

Current state Sequence to process Previous states S acd 5 cd S S Acd 2 cd S 3 d S, 2 4 S, 2, 3 S E F S

However, the token sequence ‘cd’ is processed using the parsing tree shown in FIG. 7 as shown in the following table:

Current state Sequence to process Previous states S cd 0 d S 6 S, 0 S E F S

Here, proceeding from the start state S, the parser 20 consumes a ‘c’ and proceeds to state 0, and then consumes a ‘d’ and proceeds to state 6 rather than state 1. State 6 allows processing to finish with the reduction to go back two states and replace the consumed letters by an ‘E’. Processing then returns to the start state S, where the E is processed. The E is consumed as processing proceeds to the finish state F. Thus, the token sequence ‘ce’ is parsed as having the grammatical or data type E.

It can immediately be seen how processing is made more efficient by the above embodiments of the present invention. In particular, for each possible sequence of tokens that can form the structure of data E, a path is provided that leads directly to the data being detected. Accordingly, it is not necessary to produce an additional ‘A’ at the front of sequence in order to allow for the fact that an ‘a’-type token can but need not be included in the sequence. Note that where the sequence of tokens makes up the data to be detected, the data is detected much faster.

Importantly, where the sequence of tokens does not include the data to be detected, since there is no epsilon reduction at the starting state, it is immediately detected that the data is not present. For example, if the sequence ‘gcd’ is processed, the first token in the sequence (‘g’) does not match any of the conditions of the starting state and so is discarded. Starting state S is now represented by a dotted line and no longer has a reduction associated with it. Accordingly, none of previous processing (of performing the epsilon reduction for the starting state S to add an ‘A’ to the front of the sequence, proceeding from state S to state 2, and recognizing that the condition for state 2 is not met) takes place. Consequently, a huge amount of processing is saved by avoiding the investigation of dying branches.

In some embodiments, the removal of the epsilon reduction from the starting state allows further efficiency gains. Specifically, the path from the starting state S to state 5 and the reduction of state 5 can be removed simply by changing the condition associated with the path from state S to state 2 to require an ‘a’ instead of an ‘A’. Once state 5 and its associated reduction are removed from the decision tree shown in FIG. 7, it can be seen that the total number of states is 8 (including states S and F) and the total number of reductions is 3. This compares with 8 states and 4 reductions in FIG. 3. Accordingly, in this example, not only is data detection considerably faster, the size of the cache required to store the tree is also reduced.

FIG. 8 shows how the decision tree in FIG. 6 can be improved in line with some embodiments of the present invention. In FIG. 8 the epsilon reductions for both starting state S and starting state 2 are again removed. In their place, a direct path is provided for each of the case where an optional token type is present and the case where it is not. Thus, a path is provided for the case where a ‘name’ token is present—a path including the link S-1 followed by any other states. Moreover, since the ‘company’ token is optional, two paths are provided for the path where the ‘name token’ is not present—the path including the link S-7 and the path including the link S-5.

In the same way, at least two paths are provided for the case where the ‘company’ token is present—a path including the link S-S and a path including the link 1-2. Moreover, at least two paths are provided for the case where the ‘company’ token is not present—a path including the link S-7 and a path including the link 1-4. It will be apparent that several of the paths provided to handle the optionality of the ‘company’ token correspond with the paths provided to handle the optionality of the ‘name’ token.

In common with the previous example, no state has an epsilon reduction and if data type E is included in a sequence of characters, it will always be detected by taking a single path straight through the tree according to some embodiments of the present invention. Again, if none of the conditions of the start state is matched, processing immediately ceases. This processing is highly efficient and extremely rapid compared with the prior art.

However, the present invention is not limited to the removal of epsilon reductions from all states, or even from the starting state. As an example, FIG. 9 shows a different way in which the decision tree in FIG. 6 can be improved in line with some embodiments of the present invention. In FIG. 9, the epsilon reduction for the optional ‘name’ token is removed. Thus, a path is provided for the case where a ‘name’ token is present—a path including the link S-1 followed by any other states. Moreover, since the ‘company’ token is optional, two paths are provided for the path where the ‘name token’ is not present—a path including the link S-5 and a path including the link S-6. However, in this example the epsilon reduction for the optional ‘company’ token is maintained. Indeed, two ‘company’ epsilon reductions are provided—one for the case where the ‘name’ token is present and one for the case where the ‘name’ token is not present.

FIG. 10 shows a yet further way in which the decision tree in FIG. 6 can be improved in line with some embodiments of the present invention. Specifically, in FIG. 10 the epsilon reduction for the optional ‘company’ token is removed and separate paths are provided for the cases where the optional ‘company’ token is and is not present—a path including the link 2-4 and a path including the link 2-3 respectively.

In summary, in some embodiments of the present invention, at least one epsilon reduction is removed for the case where a data structure includes at least one optional token type. Furthermore, an epsilon reduction at the starting state may be removed. In this case, if one of the starting state conditions is not met, it is immediately determined that the sequence does not include the type of data being searched for.

In some embodiments, for a data structure having a predetermined sequence of token types, at least one of which is optional, a path is provided in the tree for the predetermined sequence including the optional token type and a separate path is provided for the predetermined sequence without the optional token type. Thus, it is possible to detect predetermined data in a sequence of tokens without adding a token to the sequence to take account of an optional token type.

In some embodiments, where the sequence is revised by adding a token to the beginning of the sequence and then comparing the revised sequence with the condition or conditions of the same state, the association of a reduction with any state is prohibited.

Embodiments of the present invention has a wide variety of applications. For example, it may be used in scanning e-mails and blocks of text, such as those created in word processing packages. Moreover, it can be used in any application where sequences of characters are processed, such as in compilers and interpreters. Embodiments of the present invention may be implemented using any suitable apparatus. Such an apparatus may include, but is not limited, to data processing machines and devices, for example laptop or notebook computers, other user and server computers, and mobile communications devices, such as mobile telephones, personal digital assistants and so forth.

As an example, FIG. 11 shows an arrangement comprising a user computer 30, a notebook computer 40, and a cell phone 60, where one, some or all devices may have a processor 1 adapted to operate in accordance with the present invention. In the present example, at least the notebook computer 40 and the cell phone 60 have such a processor 1. A first user may compose a message and send it by e-mail to a second user. The second user may retrieve the message over the Internet 70 using his notebook computer 40. Upon retrieval of the message, an application embodying one embodiment of the present invention may automatically scan the message to detect whether it includes predetermined data, such as a time, a date, a name, an address and so forth. On detection of a time and a date, the application may notify the second user and provide him with the option of updating his calendar. Similarly, on detection of names, addresses, phone numbers and so forth, the application may notify the second user and provide him with the option of updating his address book. In addition, the second user may retrieve the message using his mobile phone 60 via the Internet 70 and a telecommunications base station 50. Again, an application embodying the present invention may detect predetermined data and provide the option of updating the user's records with the detected data.

It is also conceived that the present invention may be embodied using two or more different devices in some embodiments. For example, one device could carry out the lexing function and the other the parsing function.

The present invention may also be used to extract data included in mobile phone messages, such as SMS text messages and MMS messages, in some embodiments.

The present invention may also be embodied in software causing a data processing device to carry out the invention, as well as in computer-readable media on which such software is stored. Moreover, the present invention may be embodied in dedicated hardware or general-purpose hardware.

The present invention can relate to an apparatus for performing one or more of the operations described herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a machine (e.g. computer) readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CDROMs, and magneticoptical disks, readonly memories (ROMs), random access memories (RAMs), erasable programmable ROMs (EPROMs), electrically erasable programmable ROMs (EEPROMs), flash memory, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a bus.

FIG. 12 shows one example of a data processing system, such as a computer system, which may be used with the present invention. Note that while this figure illustrates various components of a data processing system, it is not intended to represent any particular architecture or manner of interconnecting the components as such details are not germane to the present invention. It will also be appreciated that network computers, personal digital assistants (PDAs), cellular telephones, handheld computers, special purpose computers, entertainment systems and other data processing systems and consumer electronic devices which have fewer components or perhaps more components may also be used with the present invention. The system of this figure may, for example, be a Macintosh computer from Apple Inc.

In some embodiments, the computer system 151 may be used as a server computer system or as a client computer system or as a web server computer system. It will be appreciated that such a computer system may be used to perform many of the functions of an Internet service provider, such as ISP 105. The computer system 151 interfaces to external systems through a modem or network interface 169. It will be appreciated that the modem or network interface 169 may be considered part of the computer system 151. This network interface 169 may be an analog modem, an ISDN modem, a cable modem, a token ring interface, a satellite transmission interface (e.g. “Direct PC”), or other interfaces for coupling a digital processing system to other digital processing systems. The computer system 151 includes a processor 153 which may be a conventional microprocessor, such as a Motorola PowerPC microprocessor or an Intel Pentium microprocessor. Memory 155 is coupled to the processor 153 by the bus 157. Memory 155 may be dynamic random access memory (DRAM) and may also include static RAM (SRAM). The bus 157 couples the processor 153 to the memory 155 and also to mass memory 163 and to display controller 159 and to the I/O (input/output) controller 165. Display controller 159 controls in the conventional manner a display on the display device 161 which may be a CRT or a liquid crystal display device. The input/output devices 169 may include a keyboard, disk drives, printers, a scanner, a digital camera, and other input and output devices, including a mouse or other pointing device. The display controller 159 and the I/O controller 165 may be implemented with conventional well known technology. The mass memory 163 is often a magnetic hard disk, an optical disk, or another form of storage for large amounts of data. Some of this data is often written, by a direct memory access process, into memory 155 during execution of software in the computer system 151. It will be appreciated that the computer system 151 is one example of many possible computer systems which have different architectures. For example, Macintosh or Wintel systems often have multiple busses, one of which may be considered to be a peripheral bus. Network computers may also be considered to be a computer system which may be used with the present invention. Network computers may not include a hard disk or other mass storage, and the executable programs are loaded from a network connection into the memory 155 for execution by the processor 153. A Web TV system, which is known in the art, may be considered to be a computer system according to the present invention, but it may not include certain features shown in FIG. 12, such as certain input or output devices. A cell phone having a suitable display and a processor and memory may also be considered to be a digital processing system or a computer system, which may be used with the present invention. A typical computer system will usually include at least a processor, memory, and a bus coupling the memory to the processor. It will also be appreciated that the computer system 151 is typically controlled by an operating system, which includes a file management system, such as a disk operating system, which is part of the operating system software.

A machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). For example, a machine-readable medium includes read only memory (“ROM”); random access memory (“RAM”); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, or other form of storage systems.

It will be apparent from this description that aspects of the inventions may be embodied, at least in part, in software. That is, the techniques may be carried out in a computer system or other data processing system in response to its processor or processing system executing sequences of instructions contained in a memory, such as RAM, etc. In various embodiments, hardwired circuitry may be used in combination with the software instructions to implement the present inventions. Thus, the techniques are not limited to any specific combination of hardware circuitry and software, nor to any particular source for the instructions executed by the data processing systems.

The foregoing description has been given by way of example only and it will be appreciated by a person skilled in the art that modifications can be made without departing from the spirit and scope of the present invention. 

1. A method of processing a sequence of tokens to detect predetermined data, comprising: receiving the sequence of tokens, wherein each token has at least one token type and the predetermined data has a structure that comprises at least one required token of first predetermined token type and further includes at least one other optional token of a second predetermined token type; comparing, by a processor in a starting state of a decision tree, a token type of a first token in the sequence with one or more conditions provided for the starting state; if one of the starting state conditions is not met, determining the predetermined data cannot be detected; and if one of said starting state conditions is met, updating the sequence by consuming the first token and proceeding to a next state of the decision tree corresponding to the met condition, if there is a token remaining in the updated sequence, comparing the token type of the token in the updated sequence with one or more conditions provided for the now current state, if one of said next state conditions is met, updating the sequence by consuming the token and proceeding to the next state corresponding to the met condition, iterating said comparing of the updated sequence and said updating of the sequence until there is no token remaining in the sequence matching a condition of the current state, determining whether a reduction is associated with the current state and, if so, using the reduction, and if no reduction is associated with the current state, determining that the predetermined data cannot be detected.
 2. A method according to claim 1, wherein at least one of one or more reductions associated with one or more states of the decision tree determines that the predetermined data has been detected.
 3. A method according to claim 1, further comprising prohibiting association with a state of a reduction in which the sequence is revised by adding another token to a beginning of the sequence and then comparing the revised sequence with conditions of the state.
 4. A method according to claim 1, wherein the predetermined data is at least one of a physical address, an IP address, an e-mail address, a time, a day, a date, and a contact number.
 5. A method according to claim 1 further comprising: providing the decision tree for detecting the predetermined data, wherein the predetermined data has a structure that comprises a predetermined sequence of token types, at least one token type being optional, wherein the tree comprises a plurality of states, each of said plurality of states being linked with at least one other state by a respective condition, and an arrangement of linked states forms a plurality of paths; and comparing token types of the sequence of tokens to respective conditions in the decision tree to match the sequence of tokens to one or more paths in the decision tree, wherein a path is provided in the decision tree for the predetermined sequence including the at least one optional token of second predetermined token type and a separate path is provided for the predetermined sequence without the at least one optional token of the second predetermined token type.
 6. A method according to claim 5, wherein the predetermined data includes a plurality of tokens of a plurality of optional token types and a separate path is provided for each of the plurality of optional token types.
 7. A method according to claim 5, further comprising: using a single path in the tree to detect the predetermined data in the sequence of tokens including the at least one optional token of the second predetermined token type.
 8. A method according to claim 1 further comprising: providing the decision tree for detecting the predetermined data, wherein the tree comprises a plurality of states, each said state being linked with at least one other state by a respective condition, the arrangement of linked states forming a plurality of paths; comparing token types of the sequence of tokens to respective conditions in the decision tree to match the sequence of tokens to one or more paths in the decision tree; and detecting the predetermined data without using an epsilon reduction to take account of the at least one other optional token of the second predetermined token type.
 9. A method according to claim 8, further comprising: detecting the predetermined data in the sequence of tokens without adding a token to the sequence of tokens to take account of the at least one other optional token of the second predetermined token type.
 10. A method according to claim 8, wherein the predetermined data includes a plurality of optional token types and the predetermined data can be detected without using an epsilon reduction to take account of any of said optional token types.
 11. A method to detect predetermined data from a sequence of tokens, the method comprising: providing, by a processor, a tree having a plurality of states, at least one of said plurality of states being associated with an epsilon reduction, each said token having a token type, and the predetermined data having a structure that comprises a predetermined sequence of token types, including at least one optional token type; removing the epsilon reduction from the tree and adding at least one additional state to form an additional path in the tree; and comparing the tokens with the additional path in the tree to detect the predetermined data when the token sequence does not include the optional token type and comparing the tokens with another path in the tree to detect the predetermined data when the token sequence includes the optional token type.
 12. A method according to claim 11, further comprising: adding a path in the tree for the predetermined data without the optional token type.
 13. A method according to claim 11, wherein the predetermined data is at least one of a physical address, an IP address, an e-mail address, a time, a day, a date, and a contact number.
 14. An apparatus for processing a sequence of tokens to detect predetermined data, the apparatus comprising: a network interface to receive the sequence of tokens, wherein each said token has a token type, and the predetermined data has a structure that comprises a predetermined sequence of token types, at least one said token type being optional; and a processor coupled to the network interface and configured to: compare, in a starting state of a decision tree, a token type of a first token in the sequence with one or more conditions provided for the starting state; if one of the starting state conditions is not met, determine the predetermined data cannot be detected; and if one of said starting state conditions is met, update the sequence by consuming the first token and proceeding to a next state of the decision tree corresponding to the met condition, if there is a token remaining in the updated sequence, compare the token type of the token in the updated sequence with one or more conditions provided for the now current state, if one of said next state conditions is met, update the sequence by consuming the token and proceeding to the next state corresponding to the met condition, iterate said comparing of the updated sequence and said updating of the sequence until there is no token remaining in the sequence matching a condition of the current state, determine whether a reduction is associated with the current state and, if so, using the reduction, and if no reduction is associated with the current state, determine that the predetermined data cannot be detected.
 15. An apparatus according to claim 14, wherein the processor is further configured to: provide the decision tree for detecting the predetermined data, the decision tree comprising a plurality of states, each said state being linked with at least one other state by a respective condition, the arrangement of linked states forming a plurality of paths; and compare the token types of the sequence of tokens to respective conditions in the decision tree to match the sequence of tokens to one or more paths in the tree, wherein the predetermined data can be detected without using an epsilon reduction to take account of at least one said optional token type.
 16. An apparatus according to claim 15, wherein the predetermined data is at least one of a physical address, an IP address, an e-mail address, a time, a day, a date, and a contact number.
 17. A non-transitory machine-readable storage medium storing executable program instructions, which, when executed cause a data processing apparatus to perform a method to process a sequence of tokens, wherein each of said sequence of tokens has a token type, and the predetermined data has a structure that comprises a predetermined sequence of token types, including at least one optional token type, the method comprising: comparing, in a starting state of a decision tree, a token type of a first token in the sequence with one or more conditions provided for the starting state; if one of the starting state conditions is not met, determining the predetermined data cannot be detected; and if one of said starting state conditions is met, updating the sequence by consuming the first token and proceeding to a next state of the decision tree corresponding to the met condition, if there is a token remaining in the updated sequence, comparing the token type of the token in the updated sequence with one or more conditions provided for the now current state, if one of said next state conditions is met, updating the sequence by consuming the token and proceeding to the next state corresponding to the met condition, iterating said comparing of the updated sequence and said updating of the sequence until there is no token remaining in the sequence matching a condition of the current state, determining whether a reduction is associated with the current state and, if so, using the reduction, and if no reduction is associated with the current state, determining that the predetermined data cannot be detected.
 18. A non-transitory machine-readable storage medium according to claim 17, the method further comprising: providing the decision tree for detecting the predetermined data, the decision tree comprising a plurality of states, each of said plurality of states being linked with at least one other state by a respective condition, an arrangement of linked states forming a plurality of paths in the tree; comparing the token types of the sequence of tokens to respective conditions in the decision tree to match the sequence of tokens to one or more paths in the tree; and detecting the predetermined data without using an epsilon reduction to take account of said at least one optional token type.
 19. A non-transitory machine-readable storage medium according to claim 18, the method further comprising: providing a path in the decision tree for the predetermined data including a token of the at least one optional token type; and providing a separate path for the predetermined data without the token of the at least one optional token type.
 20. A non-transitory machine-readable storage medium according to claim 18, wherein the predetermined data is at least one of a physical address, an IP address, an e-mail address, a time, a day, a date, and a contact number. 